<!------------------------------------------------------------------------
<!  Robot Execution Failures 
<!----------------------------------------------------------------------->
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<TITLE> Robot Execution Failures </TITLE>
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<!------------------------------------------------------------------------
<!  Title 
<!----------------------------------------------------------------------->
<H1> Robot Execution Failures </H1>


<!------------------------------------------------------------------------
<!  Data Type 
<!----------------------------------------------------------------------->
<H2>Data Type</H2>
multivariate time series

<!------------------------------------------------------------------------
<!  Abstract 
<!----------------------------------------------------------------------->
<H2>Abstract</H2>
<p>
This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals starting immediately after failure detection.
</p>

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<!  Sources
<!----------------------------------------------------------------------->
<H2> Sources</H2>
<H4> Original Owner and Donor</H4>
<PRE>
Luis Seabra Lopes and Luis M. Camarinha-Matos
Universidade Nova de Lisboa, 
Monte da Caparica, Portugal
</PRE>
<B>Date Donated: </B> April 23, 1999 

<!------------------------------------------------------------------------
<!  Data Characteristics 
<!----------------------------------------------------------------------->
<H2> Data Characteristics</H2>

The donation includes 5 datasets, each of them defining a different learning problem:
<UL>
<li> LP1: failures in approach to grasp position </li>
<li> LP2: failures in transfer of a part </li>
<li> LP3: position of part after a transfer failure </li>
<li> LP4: failures in approach to ungrasp position </li>
<li> LP5: failures in motion with part </li>
</UL>

<H4>Feature information </H4>
<P>
All features are numeric although they are integer valued only.
Each feature represents a force or a torque measured after
failure detection; each failure instance is characterized in terms
of 15 force/torque samples collected at regular time intervals
starting immediately after failure detection; 
The total observation window for each failure instance was of 315 ms.
</P>

Each example is described as follows:
<PRE>
                 class
                 Fx1	Fy1	Fz1	Tx1	Ty1	Tz1
                 Fx2	Fy2	Fz2	Tx2	Ty2	Tz2
                 ......
                 Fx15	Fy15	Fz15	Tx15	Ty15	Tz15
</PRE>
<P>
where Fx1 ... Fx15 is the evolution of force Fx in the observation
window, the same for Fy, Fz and the torques; there is a total 
of 90 features.
</P>
<H4> Number of instances in each dataset</H4>
<PRE>
   -- LP1: 88
   -- LP2: 47
   -- LP3: 47
   -- LP4: 117
   -- LP5: 164
</PRE>


<H4>Class Distribution</H4>
<PRE>
   -- LP1: 24% normal
           19% collision    
           18% front collision
           39% obstruction
   -- LP2: 43% normal
           13% front collision
           15% back collision
           11% collision to the right
           19% collision to the left
   -- LP3: 43% ok
           19% slightly moved
           32% moved
            6% lost
   -- LP4: 21% normal
           62% collision
           18% obstruction
   -- LP5: 27% normal
           16% bottom collision
           13% bottom obstruction
           29% collision in part
           16% collision in tool
</PRE>

<!------------------------------------------------------------------------
<!  Other Relevant Information 
<!----------------------------------------------------------------------->
<H2> Other Relevant Information</H2>

<H4>Feature transformation strategies</H4>

In order to improve classification accuracy, a set of five feature
transformation strategies (based on statistical summary features,
discrete Fourier transform, etc.) was defined and evaluated.
This enabled an average improvement of 20% in accuracy. The most 
accessible reference is [Seabra Lopes and Camarinha-Matos, 1998].


<!------------------------------------------------------------------------
<!  Data Format 
<!----------------------------------------------------------------------->
<H2>Data Format</H2>
The data is stored in five ASCII files, with each example in the files described as follows:
<PRE>
                 class
                 Fx1    Fy1 Fz1 Tx1 Ty1 Tz1
                 Fx2    Fy2 Fz2 Tx2 Ty2 Tz2
                 ......
                 Fx15   Fy15    Fz15    Tx15    Ty15    Tz15
</PRE>
where Fx1 ... Fx15 is the evolution of force Fx in the observation
window, the same for Fy, Fz and the torques; there is a total
of 90 features.

<!------------------------------------------------------------------------
<!  Past Usage 
<!----------------------------------------------------------------------->
<H2>Past Usage</H2>
<p>
Seabra Lopes, L. (1997) "Robot Learning at the Task Level:
a Study in the Assembly Domain", Ph.D. thesis, Universidade
Nova de Lisboa, Portugal.
</p>

<p>
Seabra Lopes, L. and L.M. Camarinha-Matos (1998) Feature 
Transformation Strategies for a Robot Learning Problem, 
"Feature Extraction, Construction and Selection. A Data Mining 
Perspective", H. Liu and H. Motoda (edrs.), 
Kluwer Academic Publishers. 
</p>

<p>
Camarinha-Matos, L.M., L. Seabra Lopes, and J. Barata (1996) 
Integration and Learning in Supervision of Flexible Assembly Systems, 
"IEEE Transactions on Robotics and Automation", 12 (2), 202-219. 
</p>

<!------------------------------------------------------------------------
<!  Signature 
<!----------------------------------------------------------------------->
<p>
<hr>
<ADDRESS>
<A href="http://kdd.ics.uci.edu/">The UCI KDD Archive</A><br>
<a href="http://www.ics.uci.edu/">Information and Computer Science</a><br>
<a href="http://www.uci.edu/">University of California, Irvine</a><br>
Irvine, CA 92697-3425 <br>
</ADDRESS> 
Last modified: March 11, 1999 </BODY>
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